An Introduction to Monte Carlo Simulations

The interaction of an electron beam with a solid can be modeled by the so-called Monte Carlo method. This technique produces a stepwise simulation of the electron trajectory by using random numbers to predict scattering angles on the basis of theoretical probability distributions or empirical models. The physical basis of electron scattering in a solid is described and two generic types of Monte Carlo model are then developed together with suggested examples of their application. An IBM PC compatible disc containing these programs is available from the author

Image Simulation for Secondary Electron Micrographs in the Scanning Electron Microscope

The interpretation of high resolution secondary electron images, and quantitative measurements of micrometer size features on integrated circuits, both require accurate modelling of the process of image formation in the scanning electron microscope. A Monte Carlo model, based on the semi-empirical theory of Salow, has been developed which permits the simultaneous computation of the secondary and backscattered yields. The physical constants necessary to apply this model can be derived from straightforward measurements of the total electron yield as a function of beam energy. On the basis of simplifying assumptions line profiles and images can then be simulated for specimens of a given geometry. The application of this technique to the problem of critical dimension metrology in the SEM is illustrated. A comparison of computed and experimental data shows that good qualitative and quantitative agreement is achieved, the quality of the comparison being limited mainly by the poor signal transfer characteristics of the video-chain of the microscope and effects such as sample charging which are not considered in the simulation

Detectors for Electron Energy Spectroscopy

The efficiency of the detector in an electron energy loss spectrometer is crucial to the performance of the system. The quality of this performance can be quantified in terms of the Detector Quantum Efficiency (DQE), the Modulation Transfer Function (MTF) and the radiation dose resistance (DR). The energy loss spectrum can be obtained either serially, by scanning the energy dispersion across a defining slit in front of a detector, or in parallel, by employing a detector or detectors with spatial resolution. The DQE, MTF and DR of serial detectors varies widely with the design chosen, but the fundamental limit to the DQE imposed by the sequential nature of the data collection process is such that serial detection, although simple, is never competitive with parallel collection. Present parallel detection schemes offer about an order of magnitude improvement in DQE over serial systems, but improvements in dynamic range, radiation resistance and fixed pattern noise are required before the full abilities of these detectors can be exploited

Monte Carlo Calculations of Secondary Electron Emission

Monte Carlo calculations of secondary electron (SE) generation have been performed using a hybrid model of the exponential decay law and cascade multiplication process. The contributions of both valence and core electron excitations, and the production of secondaries by the volume plasmon decay, have been included. The calculation has been extended to include SE\u27s with energies up to half the incident beam energy. The SE yield δSE1 component due to excitation by primary electrons, the SE yield δSE2 due to excitation by backscattered electrons, and the β coefficient are estimated using this model. Calculated SE yields, energy distributions, and β coefficients are in good agreement with the experimental data . The influence of elastic and inelastic scattering for angular distribution of the SE\u27s is discussed

Monte Carlo Calculations of Secondary Electron Emission

Monte Carlo calculations of slow secondary electron (SE) generation have been performed. Construction of a model for SE production involves three distinct steps, determining the trajectory of the incident electron, computing the rate of secondary electron generation along the trajectory of both primary and backscattered electrons, and finally calculating the secondary electron emission by using a hybrid model of the exponential decay law and cascade process. The incident electron trajectory is computed using a plural scattering Monte Carlo model. For secondary electron generation our models take into account all possible creation processes of SE resulting from the interaction of primary and backscattered electrons with free as well as bound (core) electrons and from the volume plasmon decay. Calculated SE yields, energy distributions, angular and depth distributions for Au, Ag Cu and Al are in good agreement with the experimental data available in the literature

Efficiency of the Secondary Electron Detector in the Scanning Electron Microscope

The efficiency of the secondary electron detector in the scanning electron microscope (SEM) is one of the most important factors affecting the imaging process of the SEM. To compute the detector efficiency, the electrostatic field inside a specimen chamber must be known. A simple way of performing such calculations is to use a spreadsheet program which has a built-in capability of storing and performing some operations on three-dimensional matrices. Using a spreadsheet program makes it possible to solve the Laplace equation and calculate electron trajectories in geometrically complex electrostatic fields. This technique is applied to the estimation of detector efficiency in the SEM

Depression Affects Recovery Following Distal Radius Fracture: A Latent Class Analysis

Background: Most people recover within six months following distal radius fractures (DRFs) but some experience pain and disability for one year or longer. Therefore, it is important to understand the factors that can help predict recovery. According to the biopsychosocial model of pain, psychological aspects of a condition can play important roles in explaining recovery. Objectives: To identify the recovery trajectories of patients with DRFs and to determine the degree to which depression affects these trajectories. Methods: Recovery was assessed in 318 patients using the Patient-Rated Wrist Evaluation scale at baseline, three, six, and 12 months. Demographic information was collected in addition to the Self-Administered Comorbidity Questionnaire, from which data regarding the single item pertaining to depression were extracted. Latent class analysis was used to identify the recovery trajectories. Comparisons of proportion between the emergent classes were then conducted using chi-square and Kruskal-Wallis tests. Results: The latent class analysis revealed three trajectories: rapid-recovery, slow-recovery, and non-recovery as the best fit to the data. The proportion of people that had depression was significantly greater in the non-recovery class (24%) compared to the rapid-recovery (8%) and slow-recovery classes (16%) (p Discussion: Patients who appear to be in the non-recovery class may require additional assessments, closer monitoring, supervised therapy, or other interventions to improve outcomes

Creation and validation of the 4-item BriefPCS-chronic through methodological triangulation

© 2020 The Author(s). Background: The Pain Catastrophizing Scale (PCS) is a widely used self-report tool to evaluate pain related catastrophizing. The PCS was developed using classical test theory and has been shown to be psychometrically sound among various populations. However, it\u27s current three subscales are rarely used in clinical practice, offering potential for an abbreviated version that reduces administrative burden and can be used to estimate full scale scores, yet is not bound by the inclusion of items from each subscale. Hence, the aim of the current study was to develop a unidimensional abbreviated version of the PCS through findings from qualitative, classical test theory, and newer Rasch analysis. Methods: The current cross-sectional study used data from the Quebec Pain Registry (n = 5646) to obtain PCS scores of people seeking care at tertiary chronic pain centres. To develop an abbreviated unidimensional tool, items were removed based on triangulation of qualitative review of each item and response, corrected item-total correlations, and Rasch analysis. Confirmatory factor analysis was conducted on the final remaining items to confirm the tool was assessing a single latent construct (catastrophizing). Fit was assessed using the cumulative fit index (CFI), Tucker Lewis Index (TLI), and root-mean-squared error of approximation (RMSEA). Results: After triangulation, a final abbreviated 4-item scale showed adequate model fit with a strong correlation (r \u3e 0.95) with the original scale and properties that were stable across age, sex, cause, and medicolegal status. Additionally, the brief version addressed some problematic wording on some items on the original scale. Both the original and new abbreviated tool were associated with the Beck Depression Inventory and the Brief Pain Inventory at the same magnitude. Conclusion: The abbreviated scale may allow for a decrease in administrator burden and greater clinical uptake when a quick screen for exaggerated negative orientation towards pain is needed

Chronic pain self-management support with pain science education and exercise (COMMENCE): Study protocol for a randomized controlled trial

© 2015 Miller et al. Background: Previous research suggests that self-management programs for people with chronic pain improve knowledge and self-efficacy but result in negligible effects on function. This study will investigate the effectiveness self-management support with pain science education and exercise on improving function for people with chronic pain in comparison to a wait-list control. A secondary objective is to determine which variables help to predict response to the intervention. Methods/Design: This study will be an unblinded, randomized controlled trial with 110 participants comparing a 6-week program that includes self-management support, pain science education and exercise to a wait-list control. The primary outcome will be function measured by the Short Musculoskeletal Function Assessment - Dysfunction Index. Secondary outcomes will include pain intensity measured by a numeric pain rating scale, pain interference measured by the eight-item PROMIS pain interference item-bank, how much patients are bothered by functional problems measured by the Short Musculoskeletal Function Assessment - Bother Index, catastrophic thinking measured by the Pain Catastrophizing Scale, fear of movement/re-injury measured by the 11-item Tampa Scale of Kinesiophobia, sense of perceived injustice measured by the Injustice Experience Questionnaire, self-efficacy measured by the Pain Self-Efficacy Questionnaire, pain sensitivity measured by pressure pain threshold and cold sensitivity testing, fatigue measured by a numeric fatigue rating scale, pain neurophysiology knowledge measured by the Neurophysiology of Pain Questionnaire, healthcare utilization measured by number of visits to a healthcare provider, and work status. Assessments will be completed at baseline, 7 and 18 weeks. After the 18-week assessment, the groups will crossover; however, we anticipate carry-over effects with the treatment. Therefore, data from after the crossover will be used to estimate within-group changes and to determine predictors of response that are not for direct between-group comparisons. Mixed effects modelling will be used to determine between-group differences for all primary and secondary outcomes. A series of multiple regression models will be used to determine predictors of treatment response. Discussion: This study has the potential to inform future self-management programming through evaluation of a self-management program that aims to improve function as the primary outcome. Trial registration: ClinicalTrials.gov NCT02422459 , registered on 13 April 2015

A comparison of the polytomous Rasch analysis output of RUMM2030 and R (ltm/eRm/TAM/lordif)

© 2019 The Author(s). Background: Patient-reported outcome measures developed using Classical Test Theory are commonly comprised of ordinal level items on a Likert response scale are problematic as they do not permit the results to be compared between patients. Rasch analysis provides a solution to overcome this by evaluating the measurement characteristics of the rating scales using probability estimates. This is typically achieved using commercial software dedicated to Rasch analysis however, it is possible to conduct this analysis using non-specific open source software such a R. Methods: Rasch analysis was conducted using the most commonly used commercial software package, RUMM 2030, and R, using four open-source packages, with a common data set (6-month post-injury PRWE Questionnaire responses) to evaluate the statistical results for consistency. The analysis plan followed recommendations used in a similar study supported by the software package\u27s instructions in order to obtain category thresholds, item and person fit statistics, measures of reliability and evaluate the data for construct validity, differential item functioning, local dependency and unidimensionality of the items. Results: There was substantial agreement between RUMM2030 and R with regards for most of the results, however there are some small discrepancies between the output of the two programs. Conclusions: While the differences in output between RUMM2030 and R can easily be explained by comparing the underlying statistical approaches taken by each program, there is disagreement on critical statistical decisions made by each program. This disagreement however should not be an issue as Rasch analysis requires users to apply their own subjective analysis. While researchers might expect that Rasch performed on a large sample would be a stable, two authors who complete Rasch analysis of the PRWE found somewhat dissimilar findings. So, while some variations in results may be due to samples, this paper adds that some variation in findings may be software dependent